Machine Learning Resources

These are the resources you can use to become a machine learning or deep learning engineer. All of the resources are available for free online. Please check their respective licenses.

Machine Learning Theory

• Machine Learning, Stanford University

• Machine Learning, Carnegie Mellon University

• Machine Learning, MIT

• Machine Learning, California Institute of Technology

• Machine Learning, Oxford University

• Machine Learning, Data School

Deep Learning Theory

• Deep Learning, Ian Goodfellow

• Neural Networks and Deep Learning

• Understanding LSTM Networks

• Deep Residual Learning

Forward and Backpropagation Theory and Code

• Step by Step Forwardpropagation and Backpropagation with Numbers

• Full Manual Backward Propagation with TensorFlow

• Reverse Mode Automatic Differentiation with TensorFlow

• Simple Backward Propagation with Python

• Backward Propagation from Scratch with Python

• Neural Networks Demystified with Python, Welch Labs

  • Part 1: Data and Architecture

  • Part 2: Forward Propagation

  • Part 3: Gradient Descent

  • Part 4: Backward Propagation

  • Part 5: Numerical Gradient Checking

  • Part 6: BFGS Numerical Optimization Algorithm

  • Part 7: Overfitting, Testing and Regularization

General Machine Learning with Python and Scikit-learn

• Machine Learning with scikit-learn, Data School

  • Machine Learning with scikit-learn

  • Comparing Supervised Learning Algorithm

  • Machine Learning with text

• Machine Learning with scikit-learn, Jake Vanderplas

  • Introduction to scikit-learn

  • Basic Principles

  • Linear Regression

  • Support Vector Machines

  • Regression Forests

  • Dimensionality Reduction

  • k-means Clustering

  • Density Estimation

  • Validation and Model Selection

• Decision Trees, The Grimm Scientist

• Machine Learning with scikit-learn, Andreas Mueller

  • Introduction to Machine Learning with Python

  • Scikit-learn tutorial

  • Advanced scikit-learn

• Convolutional Neural Networks with Python, Stanford

  • CS231n Convolutional Neural Networks for Visual Recognition

Convolutional Neural Networks with TensorFlow/Keras

• Deep Learning Models like VGG, Inception V3, ResNet and more in Keras

• Practical Deep Learning with Keras, Jason Brownlee

• Wide Residual Networks in Keras

• Wide ResNet in TensorLayer

• TensorLayer Official Tutorials

Reinforcement Learning Theory

• Reinforcement Learning Introduction, Nervana

• Reinforcement Learning, Sutton

  • Programming Exercises for Sutton’s Book

• Uncertainty Estimates from Dropouts

Reinforcement Learning with TensorFlow/Keras

• Using Keras with DPPG to play TORCS

• Advantage async actor-critic Algorithms (A3C) and Progressive Neural Network in TensorFlow

Recurrent Neural Networks Theory

• Understanding LSTMs

Recurrent Neural Networks with TensorFlow

• RNN Official TensorFlow Tutorials

• RNN-LSTM with TensorFlow

• Introduction to RNN in TensorFlow

• Advanced RNN guides and code

• RNN in TensorFlow with and without API

• RNNs in TensorFlow, A Practical Guide and Undocumented Features

• TensorFlow code for Latest RNN Papers

Mathematics Useful for Machine Learning

• Discrete Mathematics, MIT

• Linear Algebra, MIT

• Linear Algebra Review, Stanford

• Probability Review, Stanford

• Convex optimization overview, Stanford

• More convex optimization overview, Stanford

• Single Variable Calculus, MIT

• Practical Guide for Matrix Calculus for Deep Learning

Deep Learning Environment

• One-click AWS AMI with TensorFlow and Keras